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Record W1965937115 · doi:10.1071/mf06069

Age under-estimation in New Zealand porbeagle sharks (Lamna nasus): is there an upper limit to ages that can be determined from shark vertebrae?

2007· article· en· W1965937115 on OpenAlexaff
Malcolm P. Francis, Steven E. Campana, Cynthia M. Jones

Bibliographic record

VenueMarine and Freshwater Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsBedford Institute of Oceanography
FundersWoods Hole Oceanographic InstitutionNational Science Foundation
KeywordsSexual maturityBiologyAnatomyZoologyEcology

Abstract

fetched live from OpenAlex

Annual deposition of growth bands in vertebrae has been validated for many shark species, and is now widely regarded as the norm. However, vertebrae are part of the shark’s axial skeleton, and band deposition may stop in old sharks when somatic growth ceases. We aged vertebral sections from New Zealand porbeagle sharks (Lamna nasus) under reflected white light and using X-radiographs. Bomb radiocarbon assays supported vertebral age estimates up to ~20 years, but not at older ages. The results suggest that older porbeagles were under-aged by as much as 50% from vertebral band counts, presumably because band width declined to a point where it became unresolvable. This has important implications for growth studies on other long-lived sharks. Estimated ages at sexual maturity were 8–11 years for males and 15–18 years for females, and longevity may be ~65 years. New Zealand and North Atlantic porbeagles differ in these parameters, and in length at maturity and maximum length, suggesting genetic isolation of the two populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.318
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations105
Published2007
Admission routes1
Has abstractyes

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